Insurance Industry: Embracing Analytics Models

The insurance industry is standing at the edge of a significant technology-driven change. Technology breakthroughs in all spheres of life present a world of possibilities. However, when compared to other industries, customers are less satisfied with the digital insurance experience.

Consumers would like to have a simple and more direct experience with the insurer, which can be generated through the use of technology paired with analytics. Hence, the future belongs to those insurers who are ready to embrace technology at the earliest.

The way forward for insurers is to leverage data analytics and embrace digitisation powered by the evolution of technological advancements.

Deeper technology and analytics integration

The insurance industry is all about the selection and pricing of risks. In this regard technologies like the Internet of Things (IoT) and big data would significantly alter the standard data set being used by the insurer for accessing and analysing risks.

These technologies enable vast amount of data to be gathered and digested and insurers can be more specific, accurate and innovative in their approach. To digest all these data sources and change internal business processes is easier said than done.

Analytics can come to your rescue by providing a quantitative measure. When your existing analytics models get sharper or when new models can be created based on varied data points: you are fully utilizing the power behind the data that you have sourced.

But this process takes quite some time in the legacy environment. First, the analytics teams creates samples of data, then they build attributes and identify relevant ones, finally the model is built, tested and audited. But wait, that is just the modelling effort. The technology team then needs to often re-code the model in the technology environment and make it secure, accessible real time and hugely performant to process large amounts of data.

With newer technology advancements, analytical tools are also undergoing a sea of change. Analytical tools no longer depend on smaller samples and can easily consume and process large amounts of data. They are becoming not just cloud-aware but are adapting to harness full power and scalability of the cloud infrastructure. These tools are graduating from being just model development tools to full platforms that can help to support the full business cycle.

These technological changes have thus brought changes in areas of the core system: such as claims management, policy administration, and payments and billing. The amendments are coming in the form of modernisation of the infrastructure. Cloud infrastructure and software as a service (SaaS) are now playing an essential role in inducing the change in the workplace. While existing infrastructure and processes are embedded deep in organisation, they can sometimes be a burden for faster turnaround times and quick deployments.

Hence existing legacy infrastructure needs to be changed.

In the field of data and analytics, new age insurers need their IT teams to innovate and advance in the following areas:

Build data lakes: All client interactions should be consolidated so that the insurers can have a single view of the consumer. All data regarding an existing or even a potential customer should be available real-time in a consolidated fashion. Data can be from a variety of sources including telematics, IoT devices, mobile apps and others.

Link all the data together: Either through referential integrity or through third party matching algorithms, all data needs to be linked in order to ensure it can be used as needed.

Deploy analytical models: Ability to build, deploy and test analytical models on the collection of data. Be it simple attributes or regression models, the platform should enable access to all this linked data and then be able to deploy the models built with a fast turnaround time.

Deploying analytics models

The insurance industry as a whole is sitting on piles of data. Up to now insurers have analysed the data to evaluate risk, and even marketing strategies. However, the future demands much more. Insurers should be able to apply data analytics to deriving valuable insights for each of their customers.

Model building and tuning should not be done on a six or twelve month interval – it is part of the learning and the model should ‘adjust’ to the changes of the data – as and when they occur.

Data analytics has revolutionised how companies can analyse data. Insurers can now use that power on a real time basis to enhance their decision making and make more accurate predictions. Here are some of the ways insurers can benefit:

Better pricing decisions

Data analytics can help the insurers to access all the risk points correctly and thus correctly price the insurance product. While this is being done traditionally, insurers can now incorporate the power of big data and real time analytics to make sharper and accurate decisions.

The LexisNexis® contributory data platform can help provide the linkages within your own data, but also data about the customer’s policies and claims across insurers. As the underwriter gains more insight about the customer, he or she is in a better position to access the risk and the price of the product accordingly.

Better marketing, segmentation and process enhancement

Several marketing channels are used by insurance companies to market their products. Using data analytics models the insurance companies can better understand the segment of customers that needs to be targeted for a particular product. It can also help the insurer identify the marketing channel which would yield the best result and should be used in the specific case.

Data analytics can also help in understanding the faults in the process and accordingly help in designing and executing enhancements to the existing process.

Better customer engagement

Knowing a customer in-depth allows you to create custom products, provide custom discounts and even service the customer uniquely by addressing their needs. The banking industry has always been ahead of this curve: an example is pre-approved finance offers to customers by using sharper analytical models and creating a segment-of-one.

Insurers can similarly use analytics to uniquely service a claim of every individual and create differentiation by providing value. Recognizing and servicing a high-value customer differently can yield brand loyalty and long-time revenue and profitability.

Conclusion

There are immense benefits that can be achieved through the evolution of technological infrastructure and application of data analytics model. Use of these technologies can help the insurer achieve cost savings, efficiency and better customer engagement and retention. Insurers should take full advantage of these technologies as the true potential is just starting to show while the best is yet to come.